This paper designed a voice interactive robot system that can conveniently execute assigned service tasks in real-life scenarios. It is equipped without a microphone where users can control the robot with spoken commands; the voice commands are then recognized by a well-trained deep neural network model of automatic speech recognition (ASR), which enables the robot to execute and complete the command based on the navigation of a real-time simultaneous localization and mapping (SLAM) algorithm. The voice interaction recognition model is divided into two parts: (1) speaker separation and (2) ASR. The speaker separation is applied by a deep-learning system consisting of eight convolution layers, one LSTM layer, and two fully connected (FC) layers to separate the speaker’s voice. This model recognizes the speaker’s voice as a referrer that separates and holds the required voiceprint and removes noises from other people’s voiceprints. Its automatic speech recognition uses the novel sandwich-type conformer model with a stack of three layers, and combines convolution and self-attention to capture short-term and long-term interactions. Specifically, it contains a multi-head self-attention module to directly convert the voice data into text for command realization. The RGB-D vision-based camera uses a real-time appearance-based mapping algorithm to create the environment map and replace the localization with a visional odometer to allow the robot to navigate itself. Finally, the proposed ASR model was tested to check if the desired results will be obtained. Performance analysis was applied to determine the robot’s environment isolation and voice recognition abilities. The results showed that the practical robot system successfully completed the interactive service tasks in a real environment. This experiment demonstrates the outstanding performance with other ASR methods and voice control mobile robot systems. It also verified that the designed voice interaction recognition system enables the mobile robot to execute tasks in real-time, showing that it is a convenient way to complete the assigned service applications.

In this paper, a switching-type PD-PI controller () is proposed and a parameter selection method based on a hybrid evolution algorithm (HEA) is used to select the control parameters effectively for an automatic voltage regulator (AVR) system. The proposed switching-type PD-PI control structure combines improved transient response and reduction of steady state error to produce good global response. In the selections of parameters, HEA is applied to select an appropriate switching value and the control parameters in PD and PI controllers so that the controlled system may have good performance. In the selection of the fitness function, a new evaluation method for system performance is applied so that evolution algorithms can effectively find an appropriate parameter set. Finally, the proposed method is applied in the AVR system. From some simulation and comparison results, we can see that the proposed parameter selection method can effectively find a parameter set for the proposed switching-type PD-PI controller to yield better control performance.